Knowledge that Transforms

We introduce a pricing kernel with time-varying volatility risk aversion to explain the observed time variations in the shape of the pricing kernel. When combined with the Heston-Nandi GARCH model, this framework yields a tractable option pricing model in which the variance risk ratio (VRR) emerges as a key variable. We show that the VRR is closely linked to economic fundamentals, as well as sentiment and uncertainty measures. A novel approximation method provides analytical option pricing formulas, and we demonstrate substantial reductions in pricing errors through an empirical application to the S&P 500 index, the CBOE VIX, and option prices.

By aggregating information into stock prices, financial markets help guide the allocation of resources. We show that speculators without information about firms’ fundamentals can exploit this role of prices and profit from inflating firm valuations. Uninformed speculation is profitable because high valuations attract employees, business partners, and investors, creating value at targeted firms at the cost of diverting resources from better firms. Both large and small speculators, without pre-existing stock positions, can profit from uninformed speculation, particularly when targeting firms with moderate Q, operating in “normal” (neither hot nor cold) markets, and using performance pay or equity to attract stakeholders.

We study the interplay between a “one person-one vote” political system and a “one share-one vote” corporate governance regime. If shareholders push firms for more prosocial policies, political backlash may arise, undoing these initiatives. If public policy is frictionless, shareholder democracy becomes irrelevant: the political system fully offsets shareholder influence. With public policy frictions, prosocial corporations can mitigate regulatory shortcomings and enhance corporate public goods provision. Nevertheless, shareholder democracy can hurt citizens due to the representation problem: it favors the preferences of the wealthy. Investor diversification, pass-through voting, and corporate greenwashing have important implications for these trade-offs of shareholder democracy.

How does the increased use of data technologies, like machine learning, by financial intermediaries affect the allocation of capital towards innovation? I study this question in the context of startup financing by venture capitalists (VCs). While VCs adopting data technologies become better at screening startups similar to those in historical data, they tilt their investments towards this pool and become concurrently less likely to finance innovative startups that achieve rare major success. Plausibly exogenous variations in VCs’ screening automation suggest that these effects are causal. These findings highlight how investors’ adoption of data technologies can have real effects through innovation financing.

Research shows that access to high-quality financial services varies with local income and wealth. We study how financial firms’ internal allocation of human capital contributes to these disparities. Using a near-comprehensive panel of over 350,000 U.S. mortgage loan officers, we document large and persistent differences in productivity and performance. We find that firms’ hiring and promotion practices allocate workers with less experience or poor track records to branches serving low-income customers. Further, the consequences of poor performance differ by location: low sales, bad loans, and misconduct are more tolerated in low-income branches, exacerbating income-based disparities in financial services.

We derive optimal characteristics of contribution rates into defined contribution pension plans based on consumption-savings theory. Contribution rates should increase with age and decrease with the balance-to-income ratio. Using Swedish registry data, we show that on average, individuals save according to those principles. However, almost half of the population behaves hand-to-mouth and does not undo the mandated constant contribution rates. In a quantitative model, designing contribution rates to follow the principles implies a 1.8% welfare gain and less dispersed replacement rates, while maintaining the same average replacement rate. Results are robust to various sources of model misspecification, including temptation preferences.

We present a portfolio choice demand model that allows for the nonparametric estimation of investors’ (subjective) expectations and risk preferences. Using comprehensive 401(k)-plan-level data from 2009 through 2019, we explore heterogeneity in asset allocations using our empirical framework. We recover investors’ beliefs about each asset and examine the implications and potential sources of those beliefs. Heterogeneity in expectations across investors accounts for twice as much variation in portfolio holdings as heterogeneity in risk aversion. Belief heterogeneity is partly driven by investors’ characteristics and experiences, reflecting local sources of information such as county-level GDP and employers’ past performance.

We study how intraday and overnight components of past returns predict future stock returns from 1926 to 2019. Portfolios formed on past intraday returns display momentum without long-term reversal, whereas portfolios formed on past overnight returns display no momentum. We link this asymmetric day-night pattern to the fact that most trading occurs intraday, which has remained stable over time. Evidence from international stock markets, intraday intervals, and analyst expectations suggests that investors underreact to private information revealed through trading. This underreaction mechanism is most consistent with Hong and Stein’s (1999) theory of momentum.

This paper studies how public information, such as proxy advice, affects shareholder voting and, thus, corporate decision-making. We find that while public information improves the voting decisions of uninformed shareholders, it also induces privately informed shareholders to exit rather than to exercise their voice (vote). As a result, public information impairs information aggregation by voting but improves information aggregation by trading. Overall, public information can undermine corporate decision-making. Furthermore, slightly more precise public information can lead to a discontinuous reduction in firm value. Our results give rise to new empirical predictions and have implications for regulation.

Decentralized exchanges (DEXs) allow traders to express their willingness to pay for quick execution through a public priority fee bidding mechanism. We provide evidence that high-fee DEX trades are more informative and contribute more to price discovery. Using address-level blockchain transaction data, we show that informed traders persistently bid higher fees to secure early execution, revealing a strong willingness to pay for execution priority. Further, analysis of Ethereum mempool data demonstrates that informed traders employ a “jump bidding” strategy, placing high initial bids to deter potential competitors.